A human cytome project aims at creating a better understanding of a cellular level of biological complexity in order to allow us to close the gap between (our) molecules and the intrahuman ecosystem. Understanding the (heterogeneous) cellular level of biological organisation and complexity is (almost) within reach of present day science, which makes such a project ambitious but achievable. A human cytome project is about creating a solid translational science, not from bench to bedside, but from molecule to man.
document deals with concepts on the exploration of the Human Cytome.
The first part deals with the problems of analyzing the cytome at multiple levels of biological organization.
The second part deals with the ways of exploring and analyzing the cytome at the different levels of biological organization and complexity.
Figure 1. The bridge between the genome and the human biosystem is still not clear.
The link between genome and the human biosystem is still not clear (Figure 1). Clinical reality extends beyond the frontiers of basic science. Outside the boundaries of basic scientific research, such as genomics and proteomics, significant parts of (biological/clinical) reality remain un-explained for and not well understood. Research on the multilevel complexity of biological systems has to build the bridge between basic biological research (genes, proteins) and clinical application. See also: Drug Discovery and Development - Human Cytome Project.
"The accelerating pace of scientific and technological progress which made it possible, within just a decade, to complete the first full sequencing of the human genome � and of a growing number of other living organisms � is heralding a new era in molecular biology and genetics, in particular for human medicine. However, it is going to take a very large-scale and long-term research effort if the promises of this 'post-genomic age' are to be realised." (from The new picture of health). The purpose of critical discussion is to advance the understanding of the field. While many are spurred to criticize from competitive instincts, "a discussion which you win but which fails to help ... clarify ... should be regarded as a sheer loss." (Popper).
We may now be capable to study a low-level layer of biological integration in great detail, such as the genome or proteome, but it is in the higher-order spatial and temporal patterns of cellular (and beyond) dynamics where more answers to our questions about human pathological processes can be found. Complex disease processes express themselves in spatial and temporal patterns at higher-order levels of biological integration beyond the genome and proteome level. Genes and proteins are a fundamental part of the process, but the entire process is not represented in it entirety at the genome or proteome level. Genes are a part of a disease process, but a disease process is not entirely contained within (a) gene(s). Increasing our understanding about molecular processes on a cytomic scale is required to get a grip on complex patho-physiological processes. We have to build better bridges from basic research to clinical applications.
Higher order biological phenomena make a significant contribution to the pathway matrix from gene to disease and vice versa. However, these higher-order levels of biological integration are still being studied is a dispersed way, due to the enormous technological and scientific challenges we are facing. Several initiatives are underway (e.g. the Physiome Project), but crosslinking our research from genome to organism, from submicron to gross anatomy and physiology through an improved understanding of cellular processes at the cytome level is still missing in my opinion.
Understanding cellular patho-physiology requires exploration of cellular spatial and temporal dynamics, which can be done either in-vitro or in-vivo. Understanding biological processes and diseases requires an understanding of the structure and function of cellular processes. Two approaches allow for large scale exploration of cellular phenomena, High Content Screening and Molecular Imaging. Cellular structure and function is accessible with multiple technologies, such as microscopy (extracorporal and intracorporal), flow cytometry, magnetic resonance imaging (MRI), positron emission tomography (PET), near-infrared optical imaging, scintigraphy, and autoradiography, etc. (Heckl S, 2004). Whatever allows us a view on the human cytome wil help us to improve our understanding of human diseases, both for diagnosis as well as for treatment. We must gather more (quantitative) and better (predictive) information at multiple biological levels of biological integration to improve our understanding of human biology in health and disease.
The physiome is the quantitative description of the functioning organism in normal and pathophysiological states (Bassingthwaighte JB, 2000). A living organism and its cells are high-dimensional systems, both structural and functional. A 4-D physical space (XYZ, time) is still a formidable challenge to deal with compared to the 1-D problem of a DNA-sequence. The even higher-order feature hyperspace which is derived from this 4-D space is even further away from what we can easily comprehend. We focus the major efforts of our applied research on the level of technology we can achieve, not on the level of spatial and temporal understanding which is required. Applied research is suffering from a scale and dimensionality deficit in relation to the physical reality it should deal with. Reality does not simplify itself to adapt to the technology we use to explore biology just to please us.
The outcome of cytome research and a Human Cytome Project (HCP) should improve our
understanding of in-vivo patho-physiology in man.
We must achieve a better understanding of disease processes in:
What should we do to achieve this? Study pathological processes in:
A Human Cytome Project (HCP) shines light on multiple levels of the trajectory from basic science to clinical applications:
The rest of this article will discuss the key issues and the scientific foundation to achieve this. The technology and science we need is (almost) there, we must put it to good use.
Figure 2. Exploring a biological system from top to bottom or bottom to top.
We want to achieve a high inner resolution (detail) but at the same time we want to have a large outer resolution (overview). We want to study the entire biological system, but at a molecular resolution (Selinger DW, 2003). We could call this molecular physiology.
To understand the structure and function of a (biological) system can be done in two ways or directions. Systematicaly
studying the behavior of a biological system can be done bottom-up and top-down (Figure 2). We can look at the emerging
properties of networks derived from molecules or we can look at the properties of molecules derived from
networks. Let us compare the bottom-up approach of systems biology (system of biology) with the top-down approach of the
exploration of the cytome (biology of the system):
Systems biology is an integrated way, using computational models and pathway interference (experiments), of studying the effects of individual pathways (or drugs, targeting these pathways) on the behaviour of a whole organism or an organ/tissue.
Studying the biology of a system is an integrated way, using computational models and pathway interference (experiments), of studying the molecular function and phenotype of a whole organism or an organ/tissue and its effects on individual pathways (or drugs, targeting these pathways).
Systems biology and the biology of the system both meet and merge at the level of the dynamic network of interacting pathways.
The dynamics of cellular systems
can be explored in a global approach, which is now known as
Systems biology is not the biology of systems, it is the region between the
individual components and the system. It deals with those emerging properties
that arise when you go from the molecule to the system.
Systems biology is the in-between
between physiology or holism, which study the entire system, and molecular biology,
which only studies the molecules (reductionist approach). As such systems biology is
the glue between the genome and proteome on one side and the cytome and physiome
on the other side. The top-down approach of cytome and physiome research and the bottom-up
approach of genome and proteome research meet each other in systems biology.
I took me a while to come to terms with systems biology, as I was trained (eighties of the 20th century) in medicine and molecular biology in a traditional way. Systems biology studies biological systems systematically and extensively and in the end tries to formulate mathematical models that describe the structure of the system (Ideker T., 2001; Klapa MI, 2003; Rives A.W, 2003). The end-point of present day systems biology only takes into account infra-cellular dynamics and leaves iso- and epi-cellular phenomena to "physiology". A "systems", but top-down, approach to cytomics and physiomics is feasible with the technologies which are now emerging (e.g. HCS, HCA, fluorescent probes, biomarkers, molecular imaging,..).
Studying the physics and chemistry of protein interactions cannot ignore the spatial and temporal dynamics of cellular processes. We study nature "horizontaly", e.g. the genome or proteome, while the flux in nature goes "verticaly", through a web of intertwined pathways evolving in space and time. The focus of traditional -omics research (genomics, proteomics) is perpendicular to the flow of events in nature. The resultant vector which signifies our understanding of nature is aligned with the way we work, not with the true flow of events in nature. Molecular taxonomy or systems biology (genomics, proteomics) will not provide us with all the answers we need to know, it is however an important stepstone from molecule to man.
Biology is inherently non-linear and complex.
Many current models in biology are simplified linear approximations of reality, often derived to
fit into available technological resources. At the moment we still expect
that an oligo- or even mono-parametric low-dimensional analysis will allow us
to draw conclusions with sufficient predictive power to start working ourselves all the way up to
the disease processes in an entire organism. We are still using quite a few disease
models with a predictive deficit, which allow us to gather data at great speed and quantity,
but in the end the translation of the results into efficient treatment of
diseases fails in the majority of cases (up to 90% attrition in clinical development).
The cost of this inefficient process is becoming a burden, which both society
and the pharmaceutical industry will not be able to support indefinitely.
As "the proof is in the pudding", not in its ingredients, we have to improve the productivity of biomedical and pharmaceutical research and broaden our functional understanding of disease processes in order to prepare ourselves for the challenges facing medicine and society.
If there were no consequences on the speed of exploration in relation to the challenges medicine is facing today, the situation would of course be entirely different. In many cases, the formulation of an appropriate hypothesis is very difficult and the resulting cycle of formulating a hypothesis and verifying it is a slow and tedious process. In order to speed up the exploration of the cytome, a more open and less deterministic approach will be needed (Kell DB, 2004).
Analytical tools need to be developed which can find the needle in the haystack, without a priori knowledge or in other words we should be able to find the black cat in a dark room, without knowing or assuming that there is a black cat. An open and multi-parametric exploration of the cytome should complement the more traditional hypothesis driven scientific approach, so we can combine speed with in-depth exploration in a two-leveled approach to cytomics. The multi-dimensional and multi-scale "biological space" which we need to deal with, requires a more multi-factorial exploration than the way we explore the "biological space" at this moment. Understanding complexity cannot be achieved without facing complexity.
Many disease models have proven their value in drug discovery and pre-clinical development as we can withess in our daily lives by all the drugs which cure many diseases which were once fatal or debilitating. But we are now facing diseases which require a beter understanding of biological complexity than ever before. We still close our eyes to much of the complexity we observe; because our basic and applied disease models in drug discovery and development are not up to the challenge we are facing today. We reduce the complexity of our disease models below the limits of predictive power and meaningfulness. We must reduce the complexity of possible conclusions (improvement or deterioration), but not the quality of process feature representation or data extraction into our mathematical models. The value of a disease model does not lie in the technological complexity of the machinery we use to study it, but in its realistic representation of the disease process we want to mimic. The model is derived from reality, but reality is not derived from the model.
A disease model in drug discovery or drug development which fails to generate data and conclusions which hold into clinical development, years later, fails to fulfill its mission. Disease-models are not meant to predict future behavior of the model, but to predict the outcome of a disease and a treatment outside the model. The residual gap between the model and the disease, in complex diseases, is in many cases too big to allow for valid conclusions out of experiments with current (low-level) disease models. Due to deficient early-stage disease models, the attrition rate in pharmaceutical research is unacceptably high (80 to 90% or 4 out of 5 drugs in clinical development). Translation of the results from drug discovery and preclinical development into clinical success fails in about 90% of all developmental drugs
Every physical or biological system we try to explore shows some background variation which we cannot capture into our models. We tend to call this unaccounted variation background noise and try to eliminate it, by randomization of experiments or simply close our eyes for it. The less variation we are capable to capture into our models, the more vulnerable we are for losing subtle correlations between events. It is our inability to model complex space-time dynamics which makes us stick to simplified models which suffer from a correlation deficit in relation to reality. Biological reality does not simplify itself just to please us, but we must adapt ourselves to the dynamics of biological reality in order to increase the correlation with in vivo processes.
It is often said that the easy targets to treat are found already, but in relation to the status of scientific knowledge and understanding, "targets" were never easy to find. Disease models were just inadequate to lead to an in-depth understanding of the actual dynamics of the disease process. Just remember the concept of "miasma" before the work of Louis Pasteur and Robert Koch on infectious diseases. Only when looking back with present day knowledge we declare historical research as "easy", but we tend to forget that those scientists were fighting an uphill battle in their days. We are now facing new challenges in medicine and drug discovery for which we need a paradigm change in our approach to deal with basic and applied research.
Instead of focusing on ever further simplifying our low-dimensional and oligo-parametric disease models in order to speed them up and only increasing the complexity of the machinery to study them, we need a paradigm shift to tackle the challenges ahead of us. Increasing quantity with unmatched quality of correlation to clinical reality leads to correlation and predictive deficits. Understanding biological complexity, means capturing its complexity in the first place. We have to create a quantitative hyperspace derived from high-order spatial and temporal observations (manifold) to study the dynamics of disease processes entire cytomes and organisms. The parameterization of the observed physical process has to represent the high-dimensional (5D, XYZ, time, spectrum) and multi-scale reality underlying the disease process. Each physical or feature space can be given a coordinate system (Cartesian, polar, gauge �) which puts individual objects and processes into a relative relation to each other for further quantitative exploration.
Gathering more and better quality information about cytomic processes, will hopefully allow us to improve disease models up to a point where improved in-silico models will help us to complement in-vivo and in-vitro disease models (Sandblad B, 1992; Bassingthwaighte JB., 1995; Bassingthwaighte JB, 2000; Higgins G, 2001; Loew LM, 2001; Slepchenko BM, 2003; Takahashi, K., 2003; Berends M, 2004; De Schutter E, 2004). Cellular models are being built through the inclusion of a broad spectrum of processes and a rigorous analysis of the multiple scale nature of cellular dynamics (Ortoleva P, 2003; Weitzke EL, 2003). Modeling of spatially distributed biochemical networks can be used to model the spatial and temporal organization of intracellular processes (Giavitto JL, 2003).
The physiome is the quantitative description of the functioning organism in normal and pathophysiological states (Bassingthwaighte JB, 2000). Gradually building the �Homo (sapiens) siliconensis� or in-silico man will allow us to study and validate our disease models at different levels of biological organization. Building a rough epi-cellular model, based on our knowledge of physiology and gradually increasing the spatial and temporal functional resolution of the model by increasing its �cellularity� could allow for improving our knowledge and understanding on the way to a full-fledged in-silico model of man. (Infra-) Cellular resolution is not needed in all cases, so the model should allow for dynamic up- and down-scaling its �granularity� of structural and functional resolution in both space and time. Global, low-density models could be supplemented by a patchwork of highly defined cellular models and gradually merge into a unified multi-scale dynamic model of the spatial and temporal organization of the human cytome.
Figure 3. There is a lot of complex activity needed to build a complex cellular system (cytome) from its genes.
Source: HGP media
The functional and structural characterization (spatial, temporal) of the processes and structures leading to the phenotypical expression of the (human) cytome in a quantitative way is in my opinion the ultimate goal of an endeavor on the scale of a Human Cytome Project (HCP). We should reach a point where we are able to understand (complex) disease processes and design disease models which are capable to capture the multifactorial complexity of the in-vivo in-organism dynamics of (a) disease processes with high predictive power and correlation to clinical reality in the human biosystem (Figure 3).
This knowledge should be made broadly available for the improvement of diagnostics, disease treatments and drug discovery. It is the prerequisite to come to a better understanding of disease processes and to develop and improve treatments for new, complex and life threatening diseases for which we do not find an answer with our current genome and proteome oriented approach only.
First try to walk and then
run. Studying the (human) cytome as such is basically another way of looking at
research on cellular systems. We go from a higher level of biological
organization (cytome) to a lower one (proteome and cytome). Any research which
starts from the molecular single cell phenotypes in combination with exhaustive
bioinformatics knowledge extraction, is cytomics (Valet G, 2003). The only thing you need is something like a
flow-cytometer or a (digital) microscope to extract
the appropriate datasets to start with. Molecular Imaging
is also within reach for exploring disease processes in-vivo.
This approach can be used for for basic research, drug discovery, preclinical development or even clinical development. Generating cytome-oriented data and getting results is within reach of almost every scientist and lab. Increasing the throughput may be required for industrial research and for a large scale project, but this is not always necessary for a Proof Of Concept (P.O.C.) or for studying a specific subtopic.
To explore and understand biological processes in-vitro we can use High Content Screening (HCS) and High Content Analysis (HCA). To study cellular processes in-vivo we can use Molecular Imaging in model organisms and in man. We already have the technology at hand to "bridge the gap from bench to bedside" with translational research.
To study the entire human cytome will require a broad multidisciplinary a multinational approach, which will involve scientists from several countries and various disciplines to work on problems also from a functional and phenotypical point of view and top-down, instead of bottom-up. Both academia and industry will have to work together to avoid wasting too much time on scattered efforts and dispersed data. The organizational complexity of a large multi-center project will require a dynamic management structure in which society (politicians), funding agencies, academia and the industry participate in organizing and synchronizing the (inter)national effort (e.g. NIH Roadmap, EU DG Research,...). Managing and organizing such an endeavor is a daunting task and will require excellent managerial skills from those involved in the process, besides their scientific expertise (Collins F.S., 2003b).
The challenges of a Human Cytome Project will not allow us to concentrate on only a few techniques or systematically describing individual components, but we must keep a broad overview on cellular system genotype-phenotype correlation by multi-modal (system-wide) exploration. Increasing our understanding about molecular processes on a cytomic scale is required to get a grip on complex patho-physiological processes. We will need an open systems design in order to be able to exchange data and analyze them with a wide variety of exploratory and analytical tools in order to allow for creating a broad knowledgebase and proceed with the exploration of the cytome without wasting too much time on competing paradigms and scattered data. "The proponents of competing paradigms tend to talk at cross purposes - each paradigm is shown to satisfy more or less the criteria that it dictates for itself and to fall short of a few of those dictated by its opponent" Thomas S. Kuhn, 1962).
The project should be designed in such a way that along the road intermediate results would already provide beneficial results to medicine and drug development. Intermediate results could be derived from hotspots found during the process and worked out in more detail by groups specializing in certain areas. As such the project could consist of a large scale screening effort in combination with specific topics of immediate interest. The functional exploration of pathways involved in pathological processes, would allow us to proceed faster towards an understanding of the process involved in a disease. It is best to take a dual approach for the project, which on one side focuses on certain important diseases (cancer, AD �), and on the other side a track which focuses on cellular mechanisms such as cell cycle, replication, cell type differentiation (stem cells).� The elucidation of these cellular mechanisms, will lead to the identification of hot-spots for further research in disease process and allow for the development of new therapeutic approaches.
We manipulate and observe a process in its native environment, i.e. in its complex biological context. Therefore we must be able to observe a high dimensional environment (3D spatial plus time and spectrum) with a high inner (spatial and temporal resolving power) and wide outer resolution (field of view). No single technology will deliver all in all circumstances. A device can sample a physical space (XYZ) at a certain inner and outer resolution, which translates in a cellular system into a structure space, such as the nucleus, Golgi, mitochondria, lysozomes, membranes, organs, etc. We can sample a time axis also within a certain inner and outer resolution, which in a cellular system translates in life cycle stages such as cell division, apoptosis and cell death. The spectral axis (electromagnetic spectrum) is used to discriminate between spatially and temporally coinciding objects. It is used by means of artificially attached labels which allow us to use spectral differentiation to identify cellular structures and their dynamics. It expands the differentiating power of the probing system. Expanding our observations to entire organisms while retaining high resolving power is the next step (e.g. moleculer imaging).
We use a combination of space, time and spectrum to capture and differentiate structures and processes in and around the cells of the human cytome. The cytome is built from all different cells and cell-types of a multi-cellular organism so we multiplex our exploration over multiple cells and cell types, such as hepatocytes, fibroblasts, etc.
In the cells of the human cytome we insert structural and functional watchdogs (reporters) on different life-cycle time points and into different organelles and around cells (Deuschle K, 2005). At the moment we already have a multitude of reporters available to monitor structural and functional changes in cells (fluorescent probes, biomarkers, ...). This inserts a sampling grid or web into cells which will report structural and functional changes which we can use as signposts for further exploration. We turn cells into 4D arrays or grids for multiplexing our observations of the spatial and temporal changes of cellular metabolism and pathways. It is like using a 4D �spiderweb� to capture cellular events. Instead of extracting the 4D matrix of cellular structure and dynamics into 2D microarrays (DNA, protein �), we insert probe complexes into the in-vivo intracellular space-time web. We create an intracellular and in-vivo micro-matrix or micro-grid.
Structural and functional changes in cells will cause a space-time "ripple" in the structural and functional steady state of the cell and if one of the reporters is in proximity of the status change it will provide us with a starting point for further exploration. A living cell is not a static structure, buth an oscillating complex of both structural and functional events. The watchdogs are the bait to capture changes and act as signposts from which to spread out our cytome exploration. We could see them as the starting point (seeds) of a shotgun approach or the threads of a spiderweb for cytome exploration.
The spatial and temporal density and sensitivity of our reporters and their structural and functional distribution throughout the cytome will define our ability to capture small changes in the web of metabolic processes in cells. At least we capture changes in living cells (in-vivo or in-vitro), closely aligned with the space-time dynamics of the physilogicla process. We should try to align the watchdogs with hot-spots of cellular structure and function. The density and distribution of watchdogs is a dynamic system, which can be in-homogeneously expanded or collapsed depending on the focus of research.
Multiple techniques are available to maniplulate the dynamics of cellular systems, either for single cells or for the entire cytome of an organism, e.g. RNAi, chemical genomics, morpholinos, aptamers, etc. . By monitoring the functional and structural changes we can learn about the underlying in vivo processes in a complex biosystem.
RNA interference (RNAi) can silence gene expression and can be used to inhibit the function of any chosen target gene (Banan M, 2004; Campbell TN, 2004; Fire A., 1998; Fraser A. 2004; Mello CC, 2004; Mocellin, 2004). Large scale RNAi creening is now within reach (Sachse C, 2005). This technique can be used to study the effect of in-vivo gene silencing on the expressed phenotype (watchdog monitoring) in a transient way, both in individual cells as well as in organisms.
Aptamers are small molecules that can bind to another molecule (Ellington, 1990). We can use DNA or RNA aptamers and protein aptamers to interfere with cellular processses (Crawford M, 2003; Toulme, 2004). Morpholino oligos are used to block access of other molecules to specific sequences within nucleic acid molecules (Ekker SC., 2000. They can block access of other molecules to small (~25 base) regions of ribonucleic acid (RNA). Morpholinos are sometimes referred to as PMO, an acronym for phosphorodiamidate morpholino oligo (Summerton J., 1999; Achenbach TV, 2003).
Chemical genetics is the study of biological systems using small molecule ('chemical') intervention, instead of only genetic intervention. Cell-permeable and selective small molecules can be used to perturb protein function rapidly, reversibly and conditionally with temporal and quantitative control in any biological system (Spring DR., 2005; Haggarty SJ. 2005).
Stem cells can be made to differentiate into different cell types and the differentiation process montired for spatial and temporal changes and irregularities. By using stem cells we can mimic (and avoid) taking multiple biopsies at different life stages of an individual and its cells. The resulting cell types can be used for multiplexing functional and structural research of intracellular processes. RNAi can be applied to stem cell research to study stem cell function (Zou GM, 2005).
We need technology (probes and detectors) to monitor and manipulate a complex biological system, ranging from mono-cellular up to complete cytomes (organisms).
Human biology can be explored at multiple levels and scales of biological organization, by using many different techniques, such as CT, MRI, PET, LM, EM, etc. each providing us with a structural and functional subset of the physical phenomena going on inside the human body. In this section I will focus on the cellular level and on microscopy-based techinques and Molecular Imaging techniques. Achieving (sub-) cellular resolution poses thechnological challenges, which can be met by the combined use of multiple techniques and instruments.
Non-invasive molecular imaging modalities such as optical imaging (OI), magnetic resonance imaging (MRI), MR spectroscopy and positron emission tomography (PET) allow in-vivo assessment of metabolic changes in animals and humans. While CT and MRI provide anatomical information, optical fluorescence and bioluminescence imaging and especially PET reveal functional information, in case of PET even in the picomolar range.
The necessity to explore the cellular level of the human physiome poses some demands on the spatial, spectral and temporal inner and outer resolution which has to be met by the technology used to extract content from the cell. However there is no one-on-one overlap between the biological structure and activity at the level of the cytome and our technological means to explore this level. Life does not remodel its physical properties to adapt to our exploratory capabilities. The alignment of the scale and dimensions of cellular physics with our technological means to explore is still far from perfect. The discontinuities and imperfections in our exploratory capacity are a cause of the fragmentation of our knowledge and understanding of the structure and dynamics of the cytome and its cells. Our knowledge is aligned with our technology, not with the underlying biology.
Every scientific challenge leads to the improvement of existing technologies and the development of new technologies (Tsien R, 2003). Technology to explore the cytome is already available today and exciting developments in image and flow based cytometry are going on at the moment. The dynamics of living cells is now being studied in great detail by using fluorescent imaging microscopy techniques and many sophisticated light microscopy techniques are now available (Giuliano KA, 1998; Tsien RY, 1998; Rustom A, 2000; Emptage NJ., 2001; Haraguchi T. 2002; Gerlich D, 2003b; Iborra F, 2003; Michalet, X., 2002; Michalet, X., 2003; Stephens DJ, 2003; Zimmermann T, 2003). Studying intra-vital processes is possible by using microscopy (Lawler C, 2003). Quantitative microscopy requires a clear understanding of the basic principles of digital microscopy and sampling to start with, which goes beyond the principles of the Nyquist sampling theorem (Young IT., 1988).
Advanced microscopy techniques are available to study the morphological and temporal events in cells, such as confocal and laser scanning microscopy (LSM), digital microscopy, spectral imaging, Fluorescence Lifetime Imaging Microscopy (FLIM), Fluorescence Resonance Energy Transfer (FRET) and Fluorescence Recovery After Photobleaching (FRAP) (Cole, N. B. 1996; Truong K, 2001, Larijani B, 2003; Vermeer JE, 2004). Spectral imaging microscopy and FRET analysis are applied to cytomics (Haraguchi T, 2002; Ecker RC, 2004). Fluorescent speckle microscopy (FSM) is used to study the cytoskeleton in living cells (Waterman-Storer CM, 2002; Adams MC, 2003; Danuser G, 2003).
Laser scanning (LSM) and wide-field microscopes (WFM) allow for studying molecular localisation and dynamics in cells and tissues (Andrews PD, 2002). Confocal and multiphoton microscopy allow for the exploration of cells in 3D (Peti-Peterdi J, 2003). Multiphoton microscopy allows for studying the dynamics of spatial, spectral and temporal phenomena in live cells with reduced photo toxicity (Williams RM, 1994; Piston DW, 1999; Piston DW. 1999b; White JG, 2001).
Green fluorescent protein (GFP) expression is being used to monitor gene expression and protein localization in living organisms (Shimomura O, 1962; Chalfie M, 1994; Stearns T. 1995; Lippincott-Schwartz J, 2001; Dundr M, 2002; Paris S, 2004). Using GFP in combination with time-resolved microscopy allows studying the dynamic interactions of sub-cellular structures in living cells (Goud B., 1992; Rustom A, 2000). Labelling of bio-molecules by quantum dots now allows for a new approach to multicolour optical coding for biological assays and studying the intracellular dynamics of metabolic processes (Chan WC, 1998; Han M, 2001; Michalet, X., 2001; Chan WC, 2002; Watson A, 2003; Alivisatos, AP, 2004; Zorov DB, 2004).
The resolving power of optical microscopy beyond the diffraction barrier is a new and interesting development, which will lead into so-called super-resolving fluorescence microscopy (Iketaki Y, 2003). New microscopy techniques such as standing wave microscopy, 4Pi confocal microscopy, I5M and structured illumination are breaking the diffraction barrier and allow for improving the resolving power of optical microscopy (Gustafsson MG., 1999; Egner, A., 2004). We are now heading towards fluorescence nanoscopy, which will improve spatial resolution far below 150 nm in the focal plane and 500 nm along the optical axis (Hell SW., 2003; Hell SW, 2004).
Exploring ion flux in cells, such as for Calcium, is already available for a long time (Tsien R, 1981, Tsien R 1990; Cornelissen, F, 1993). Locating the spatial and temporal distribution of Ca2+ signals within the cytosol and organelles is possible by using GFP (Miyawaki A, 1997). Fluorescence ratio imaging is being used to study the dynamics of intracellular Ca2+ and pH (Bright GR, 1989; Silver RB., 1998; Fan GY, 1999; Silver RB., 2003; Bers DM., 2003).
Microscopy is being used to study Mitochondrial Membrane Potentials (MMP) and the spatial and temporal dynamics of mitochondria (Zhang H, 2001; Pham NA, 2004). The distribution of H+ ions across membrane-bound organelles can be studied by using pH-sensitive GFP (Llopis J, 1998)
Electron Microscopy allows studying cells almost down to the atomic level. Atomic Force Microscopy (AFM) allows studying the structure of molecules (Alexander, S., 1989; Drake B, 1989; Hoh, J.H., 1992; McNally HA, 2004). Multiple techniques can be used, such as combining AFM for imaging living cells and compare this with Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM) (Braet F, 2001).
High Content Screening (HCS) is available for high speed and large volume screening of protein function in intact cells and tissues (Van Osta P., 2000; Van Osta P., 2000b; Liebel U, 2003; Conrad C, 2004; Abraham VC, 2004; Van Osta P., 2004). New research methods are bridging the gap between neuroradiology and neurohistology, such as magnetic resonance imaging (MRI), positron emission tomography (PET), near-infrared optical imaging, scintigraphy, and autoradiography (Heckl S, 2004).
Processes can be monitored by using bioluminescence to monitor gene expression in living mammals (Contag CH, 1997; Zhang W., 2001). Both bioluminescence and fluorescence technology can be used for studying disease processes and biology in vivo (Choy G, 2003). Multimodality reporter gene imaging of different molecular-genetic processes using fluorescence, bioluminescence (BLI), and nuclear imaging techniques is also feasible (Ponomarev V, 2004).
Flow Cytometry allows us to study the dynamics of cellular processes in great detail (Perfetto SP, 2004; Voskova D, 2003; Roederer M, 2004). Interesting developments are leading to fast imaging in flow (George TC, 2004). Combining both image and flow based cytometry can shed new light on cellular processes (Bassoe C.F., 2003).
In recent years tools came available for establishing the actions of agents in the complex biochemical networks characteristic of fully assembled living systems (Fowler BA., 2005; Turner SM, 2005). Molecular Imaging is broadly defined as the characterization and measurement of biological processes in living animals at the cellular and molecular level. Noninvasive "multimodal" in vivo imaging is not just becoming standard practice in the clinic, but is rapidly changing the evolving field of experimental imaging of genetic expression ("molecular imaging"). The development of multimodality methodology based on nuclear medicine (NM), positron emission tomography (PET) imaging, magnetic resonance imaging (MRI), and optical imaging is the single biggest focus in many imaging and cancer centers worldwide and is bringing together researchers and engineers from the far-ranging fields of molecular pharmacology to nanotechnology engineering. The rapid growth of in vivo multimodality imaging arises from the convergence of established fields of in vivo imaging technologies with molecular and cell biology.
The role of any multimodal imaging approach ideally should provide the exact localization,
extent, and metabolic activity of the target tissue, yield the tissue flow and function or functional changes
within the surrounding tissues, and in the topic of imaging or screening, highlight any pathognomonic changes
leading to eventual disease.
High-resolution scanners are bing used for imaging small animals of use in the many new molecular
imaging centers appearing worldwide for reporter gene-expression imaging studies in small animals.
The coupling of nuclear and optical reporter genes represents only the beginning of far wider applications
of this research. Initially, fluorescence and bioluminescence optical imaging, in providing a cheaper
alternative to the more intricate microPET, microSPECT, and microMRI scanners, have gathered the most
focus to date. In the end, however, the volumetric tomographic technologies, which offer deep tissue
penetration and high spatial resolution, will be married with noninvasive optical imaging.
Molecular Imaging (will) bridge(s) the gap from (sub-)cellular to macrocellular anatomy and physiology, allowing us to study the dynamics of cellular process in-vivo (Kelloff GJ, 2005; Kola I, 2005).
The ability to accurately measure the in-vivo molecular response to a drug allows to demonstrate the pharmacological effects in a smaller number of individuals and at lower doses. This forms the basis for the Proof-of-Concept (POC) trials that aim to confirm and describe the desired pharmacodynamic (PD) effect in man before starting traditional clinical trials. Changes in molecular markers can often be demonstrated at a fraction of the therapeutic dose. Microdosing is a technique whereby experimental compounds are administered to humans in very small doses (typically 1/100th of a therapeutic dose) (Wilding IR, 2005). The drug's pharmacokinetics are characterized through the use of very sensitive detection techniques, such as accelerator mass spectrometry (AMS) and PET (Bergstrom M, 2003). Human microdosing aims to reduce attrition at Phase I clinical trials.
In order to come to come to a quantitative understanding of the dynamics of in-vivo cellular processes image processing, methods for object detection, motion estimation and quantisation are required. The first step in this process is the extraction of image components related to biological meaningful entities, e.g. nuclei, organelles etc., Secondly quantitative features are applied to the selected objects, such as area, volume, intensity, texture parameters, etc. Finally a classification is done, based on separating, clustering, etc. of these quantitative features.
New image analysis and quantification techniques are constantly developed and will enable us to analyze the images generated by the imaging systems (Van Osta P, 2002; Eils R, 2003; Nattkemper TW, 2004; Wurflinger T, 2004). The quantification of high-dimensional datasets is a prerequisite to improve our understanding of cellular dynamics (Gerlich D, 2003; Roux P, 2004; Gerster AO, 2004). Managing tools for classifiers and feature selection methods for elimination of non-informative features are being developed to manage the information content and size of large datasets (Leray, P., 1999; Jain, A.K., 2000; Murphy, R.F., 2002; Chen, X., 2003; Huang, K., 2003; Valet GK, 2004)
Imaging principles based on
physics and human vision principles allow for the development of new and
interesting algorithms (Geusebroek J.M., 2001; Geusebroek J. M., 2003; Geusebroek
J. M., 2003b; Geusebroek J. M., 2005). The necessary
increase of computing power requires both a solution at the level of
computation as increasing the processing capacity (Seinstra
The development of new and improved algorithms will allow us to extract quantitative data to create the high-dimensional feature spaces for further analysis.
This section will be expanded in a separate document on a Research Management System (RMS).
My personal interest was to build a framework in which acquisition, detection and quantification are designed as modules each using plug-ins to do the actual work (Van Osta P, 2004) and which in the end could manage a truly massive exploration of the human cytome. Two approaches allow for large scale exploration of cellular phenomena, High Content Screening and the emerging field of Molecular Imaging
The basic principle of a digital imaging system is to create a digital in-silico representation of the spatial, temporal and spectral physical process which is being studied. In order to achieve this we try to let down a sampling grid on the biological specimen. The physical layout of this sampling grid in reality is never a precise isomorphic cubical sampling pattern. The temporal and spectral sampling inner and outer resolution is determined by the physical characteristics of the sample and the interaction with the detection technology being used.
The extracted objects are sent to a quantification module which attaches an array of quantitative descriptors (shape, density �) to each object. Objects belonging to the same biological entity are tagged to allow for a linked exploration of the feature space created for each individual object (Van Osta P., 2000; Van Osta P., 2002b, Van Osta P., 2004). The resulting data arrays can be fed into analytical tools appropriate for analysing a high dimensional linked feature space or feature hyperspace.
Managing and analyzing large datasets in a multidimensional linked feature space or hyperspace will require a change in the way we look at data analysis and data handling. Analyzing a multidimensional feature space is computationally very demanding compared to a qualitative exploration of a 3D image. We often try to reduce the complexity of our datasets before we feed them into analytical engines, but sometimes this is a �reductio ad absurdum�, below the level of meaningfulness. We have to create tools to be able to understand high-dimensional feature �manifolds� if we want to capture the wealth of data cell based research can provide. Transforming a high-dimensional physical space into an even higher order feature space requires an advanced approach to data analysis.
The conclusion of an experiment may be summarized in two words, either �disease� or �healthy�, but the underlying high-dimensional feature space requires a high-dimensional multiparametric analysis. Data reduction should only occur at the level of the conclusion, not at the level of the quantitative representation of the process. The alignment of a feature manifold with the multi-scale and multidimensional biological process would allow us to capture enough information to increase the correlation of our analysis with the space-time continuum of a biological process.
Building the multidimensional matrix of the web of cross-relations between the different levels of biological organization, from the genome, over the proteome, cytome all the way up to the organism and its environment, while studying each level in a structural (phenotype) and functional way, will allow us to understand the mechanisms of pathological processes and find new treatments and better diagnostics tools. A systematic descriptive approach without a functional complement is like running around blind and it takes too long to find out about the overall mechanisms of a pathological process or to find distant consequences of a minute change in the pathway matrix.
We should also get serious on a better integration of functional knowledge gathered at several biological levels, as the scattered data are a problem in coming to a better understanding of biological processes. The current data storage models are not capable of dealing with heterogeneous data in a way which allows for in-depth cross-exploration. Data management systems (data warehouses) will need to broaden their scope in order to deal with a wide variety of data sources and models. Storage is not the main issues, the use and exploration of heterogeneous data is the centerpiece of scientific data management. Data originating from different organizational levels, such as genomic (DNA sequences), proteomic (protein structure) and cytomic (cell) data should be linked. Data originating from different modes of exploration, such as LM, EM, NMR and CT should be made cross-accessible. Problems to link knowledge originating from different levels of biological integration is mainly due to a failure of multi scale or multilevel integration of scientific knowledge, from individual gene to the entire organism, with appropriate attention to functional processes at each biological level of integration.
Both basic and applied research should adhere to the highest quality standards without sacrificing exploratory freedom and scientific inspiration. On the experimental side, standardization of experimental procedures and quality control is of great importance to be able to compare and link the results from multiple research-centers. But quality is not only a matter of experimental procedures and instrumentation, but also of disease model validation and verifying the congruence of a model with clinical reality.
We need to design procedures for instrument set-up and calibration (Lerner JM, 2004). We need to define experimental protocols (reagents�) in order to be able to compare experiments. In addition we need to standardize data exchange procedures and standards such as the CellML language, CytometryML, Digital Imaging and Communications in Medicine (DICOM), Open Microscopy Environment (OME XML) and the Flow Cytometry Standard (FCS) (Murphy RF, 1984; Seamer LC, 1997; Leif RC, 2003; Swedlow JR, 2003; Horn RJ. 2004; Samei E, 2004). The purpose of CellML is to store and exchange computer-based mathematical models (Autumn A., 2003. A file format such as the Image Cytometry Standard (ICS v.1 and 2) provides for a very flexible way to store and retrieve multi-dimensional image data (Dean P., 1990).
Laboratory Information Manangement Systems (LIMS) and automated lab data management provide support for sample an data handling, which is an important aspect of every large scale automated research project. Laboratory Information Management Systems (LIMS) are information management systems designed specifically for the analytical laboratory. This includes research and development (R&D) labs, in-process testing labs, quality assurance (QA) labs, and more. Typically, LIMS connect the analytical instruments in the lab to one or more workstations or personal computers (PC). The rise of Informatics, coupled with the increasing speed and complexity of the analytical instruments, is driving more sophisticated data manipulation and data warehousing tools that work hand-in-glove with LIMS to manage and report laboratory data with ever greater accuracy and efficiency.
From clinical practice we could borrow some principles of quality assurance (QA) and quality control (QC). If we want to speed up the transfer of knowledge from basic research to clinical applications for the benefit of mankind, we should create Standard Operating Procedures (SOPs). Good Laboratory Practices (GLP) (FDA GLP, OECD GLP) would improve the quality of our methods used in the laboratory. For the instrumentation used in the research projects which constitute a Human Cytome Project (HCP), attention should be given to the FDA Title 21 Code of Federal Regulations (FDA Title 21 CFR Part 11) on Electronic Records and Electronic Signatures. In the design and development of instrumentation, Current Good Manufacturing Practices (cGMP) are valuable guidelines for manufacturing quality. The Good Automated Manufacturing Practice (GAMP) Guide for Validation of Automated Systems in Pharmaceutical Manufacture could be another source of information in order to guarantee the quality of the data coming out of the Human Cytome Project (HCP).
The methods used for data
analysis, data presentation and visualization need to be standardized also. We
need to define quality assurance (QA) and quality control (QC) procedures and standards which can be used
by laboratories to test their procedures. A project on this scale requires a
registration and repository of cell types and cell lines
ECCC). This way of working is already
implemented for clinical diagnosis, by organizations such as the
European Working Group on Clinical Cell Analysis
(EWGCCA), which could help to implement standards and procedures for a Human Cytome Project (HCP).
Reference standards for our measurements can be developed and provided by institutions such as the Institute for Reference Materials and Measurements (IRMM) in Europe, one of the seven institutes of the Joint Research Centre (JRC), a Directorate-General of the European Commission (EC).
The complexity of diseases requires an improvement of our understanding of high-order biological processes in man. We cannnot close our eyes to the unmet needs of many patients, which are in need of new and better therapies. New ways to deal with disease processes are required if we want to succeed in the war against diseases threatening mankind.
References can be found here
I am indebted, for their pioneering work on automated digital microscopy and High Content Screening (HCS) (1988-2001), to my former colleaguas at Janssen Pharmaceutica (1997-2001), such as Frans Cornelissen, Hugo Geerts, Jan-Mark Geusebroek, Roger Nuyens, Rony Nuydens, Luk Ver Donck and their colleaguas.
Many thanks also to the pioneers of Nanovid microscopy at Janssen Pharmaceutica, Marc De Brabander, Jan De Mey, Hugo Geerts, Marc Moeremans, Rony Nuydens and their colleagues. I also want to thank all those scientists who have helped me with general information and articles.
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The author of this webpage is Peter Van Osta, MD.
Latest revision on 15 August 2008